Eigenwaves
2022
- 81Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage81
- Abstract Views39
- Downloads38
- Plays4
Poster Description
By representing audio information as a many-dimensional vector, it is possible to derive the characteristic eigenvectors of this audio through data manipulation techniques. From these “eigenwaves”, sounds can be identified or created. The identification of audio waves using eigenwaves has the potential to be useful in many practical applications ranging from human voice recognition to the creation of authentic-sounding computer-generated voices. Some limitations of the proposed method also will be described.
Bibliographic Details
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